The current rapid progress of design rules and processing technology in semiconductor manufacturing makes yield and characteristics analysis more and more difficult and complicated. Typically production data is stored in one database, and non-production data is stored in another database. Data analysis is performed on the production data, using a variety of points for comparison. This data analysis may show abnormalities in production trends. Non-production data is separately gathered and subjected to separate analysis of conditions and identification of significant trends.
This data analysis is important to the quality of the manufactured material. Variations in production conditions can cause entire lots of product to be discarded, wasting valuable production time and money. Quick data analysis may avoid wholesale scrapping of product. Unfortunately the large magnitude of data that is collected hinders a quick analysis that would be meaningful to production goals.
Further compounding the analysis problem is that factors typically thought to be non-production are not considered in the analysis. Environmental measurements which can greatly affect the quality of manufacturing end-product are just one example of these factors. Even when one is able to qualitatively measure these factors, connecting that meaningfully to other measurements considered to be non-production for the purposes of data analysis requires a user to manually examine the data for commonalities and correlate the data based on those. Further combining that combination with actual production data greatly compounds the amount of data as well as compounding the inability to perform meaningful and timely data analysis.
What is needed is a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis.